Are the GLM-5.2 weights open and can I run it locally?

Yes — GLM-5.2 is released under an MIT licence with no regional restrictions, and the weights are on Hugging Face. Z.ai supports local deployment via SGLang, vLLM, Transformers, KTransformers, Unsloth, and Ascend NPU frameworks.

What does GLM-5.2 cost and what is its context window?

Via the Z.ai API the price is $0.06 per million input tokens and $7.00 per million output tokens, with a 1,048,576-token (roughly 1M) context window.

How does GLM-5.2 differ from GLM-5.1?

GLM-5.2 adds a stable 1M-token context (versus a shorter window in 5.1), stronger coding scores across SWE-bench Pro and Terminal Bench, flexible reasoning effort levels, and architectural changes — IndexShare and an improved MTP layer — that reduce per-token compute at long contexts and increase speculative decoding acceptance length.

▲

21.7%

this week

GLM 5.2

9057°

#llm #z-ai #reasoning #coding #agents #long context

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Guides for this model

Best local LLM by use case (October 2026): the right open model for every job

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Z.ai | 1,048,576 token context | $0.04/M input, $9.00/M output. GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering, and complex multi-step automation. Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is particularly strong at coding and tool use across long-running tasks, able to maintain engineering context and follow standards consistently through a full development workflow, from requirements to multi-platform deployment, in a single task.

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

GLM-5.2 is Z.ai's flagship reasoning model built for long-horizon work: project-scale software engineering, multi-step automation, and agentic pipelines that need to hold context across a full 1M-token window. Open weights under an MIT licence with no regional restrictions set it apart from most frontier-class competitors. If your work involves sustained coding tasks or complex tool-use chains rather than quick one-shot queries, this is worth a serious look.

Known for

1M-token context that holds up across long, multi-file engineering tasks
Adjustable reasoning effort levels, including a maximum xhigh setting
Strong real-world coding benchmarks: SWE-bench Pro, Terminal Bench, FrontierSWE
MIT licence, open weights, no regional access restrictions
Architectural efficiency gains (IndexShare, improved speculative decoding) for long contexts

Watch out for

Output pricing at $7.00/M tokens adds up fast on long reasoning chains; budget accordingly.
The model is large; local deployment requires serious hardware and a supported inference framework.
Weights are open but self-hosting a 1M-context run demands infrastructure most individuals do not have.

Best for

Prompts to try

Full-stack feature build

You are working in a monorepo with a Node.js REST API and a React frontend. Add a user notification system: design the database schema, write the API endpoints with input validation, implement the frontend component that polls for new notifications, and write integration tests. Follow existing code conventions and explain each decision as you go.

Multi-step deployment plan

I have a Python FastAPI service that currently runs on a single VM. Plan and implement a migration to a containerised, multi-platform deployment: write the Dockerfile, a docker-compose file for local development, a Kubernetes manifest for production, and a GitHub Actions workflow that builds, tests and deploys on push to main. Flag any assumptions you make.

Hard reasoning puzzle

Five engineers each own a different programming language, drink a different beverage, and sit in a row of five desks. The Rust engineer sits next to the one who drinks espresso. The Python engineer is at one end. The Go engineer drinks tea. The TypeScript engineer sits immediately left of the Java engineer. The coffee drinker is not at either end. Who drinks water, and at which desk do they sit? Show your reasoning step by step.

Compare with

GLM 5.3 FlashX
API-only model
1,048,576 token context · $0.37/M input, $1.25/M output
GLM 5V Turbo
API-only model
202,752 token context · $1.20/M input, $4.00/M output
GLM 5 Turbo
API-only model
202,752 token context · $1.20/M input, $4.00/M output
GLM 5.3 Prime
API-only model
1,000,000 token context · $2.80/M input, $8.80/M output

More text models from z-ai

Questions people ask

Are the GLM-5.2 weights open and can I run it locally?: Yes — GLM-5.2 is released under an MIT licence with no regional restrictions, and the weights are on Hugging Face. Z.ai supports local deployment via SGLang, vLLM, Transformers, KTransformers, Unsloth, and Ascend NPU frameworks.
What does GLM-5.2 cost and what is its context window?: Via the Z.ai API the price is $0.06 per million input tokens and $7.00 per million output tokens, with a 1,048,576-token (roughly 1M) context window.
How does GLM-5.2 differ from GLM-5.1?: GLM-5.2 adds a stable 1M-token context (versus a shorter window in 5.1), stronger coding scores across SWE-bench Pro and Terminal Bench, flexible reasoning effort levels, and architectural changes — IndexShare and an improved MTP layer — that reduce per-token compute at long contexts and increase speculative decoding acceptance length.

Captain's verdict
A rare combination: frontier-class long-horizon coding and agentic capability, MIT-licensed, with weights you can actually run yourself.

Model card on Hugging Face

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Also ranked: #18 Hottest Text / LLM model · #18 Hottest Text / LLM text model

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GLM 5.2 Popularity Trend

Heat score since 16 Jun 2026

Heat score

9,057°

This week

▲

21.7%

9,030° then, 9,057° now

This month

▲

155%

8,931° then, 9,057° now

Past year

n/a

Wasn't released/tracked

7,000°

Floor

The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.

+1,739°

OpenRouter tokens

6,248,561,667,512 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 624,856 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).

+318°

Adoption rate

About 17,563 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.

Show detailed breakdown

Every number on this site can be checked. This model's heat score is 9,057°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.

1
Start at the floor.
Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.
7,000°
running total
2
Add the downloads.
624,856 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.
+1,739°
8,739°
running total
3
Add the adoption rate.
About 17,563 downloads a day right now, measured between two readings, at a quarter of the weight.
+318°
9,057°
running total
=
The heat score.
Where this model stands on every list of this site. Nothing else adjusts it.
9,057°

Glossary: the terms used here

Heat score: One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor: The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes: Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.

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